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Although it is now possible to integrate many millions oftransistors on a single chip, traditional digital circuittechnology is now reaching its limits, facing problems of cost andtechnical efficiency when scaled down to ever-smaller featuresizes. The analysis of biological neural systems, especially forvisual processing, has allowed engineers to better understand howcomplex networks can effectively process large amounts ofinformation, whilst dealing with difficult computationalchallenges. Analog and parallel processing are key characteristics ofbiological neural networks. Analog VLSI circuits…mehr

Produktbeschreibung
Although it is now possible to integrate many millions oftransistors on a single chip, traditional digital circuittechnology is now reaching its limits, facing problems of cost andtechnical efficiency when scaled down to ever-smaller featuresizes. The analysis of biological neural systems, especially forvisual processing, has allowed engineers to better understand howcomplex networks can effectively process large amounts ofinformation, whilst dealing with difficult computationalchallenges. Analog and parallel processing are key characteristics ofbiological neural networks. Analog VLSI circuits using the samefeatures can therefore be developed to emulate brain-styleprocessing. Using standard CMOS technology, they can be cheaplymanufactured, permitting efficient industrial and consumerapplications in robotics and mobile electronics. This book explores the theory, design and implementation ofanalog VLSI circuits, inspired by visual motion processing inbiological neural networks. Using a novel approach pioneered by theauthor himself, Stocker explains in detail the construction of aseries of electronic chips, providing the reader with a valuablepractical insight into the technology. Analog VLSI Circuits for the Perception of VisualMotion: * analyses the computational problems in visual motionperception; * examines the issue of optimization in analog networks throughhigh level processes such as motion segmentation and selectiveattention; * demonstrates network implementation in analog VLSI CMOStechnology to provide computationally efficient devices; * sets out measurements of final hardware implementation; * illustrates the similarities of the presented circuits with thehuman visual motion perception system; * includes an accompanying website with video clips of circuitsunder real-time visual conditions and additional supplementarymaterial. With a complete review of all existing neuromorphic analog VLSIsystems for visual motion sensing, Analog VLSI Circuits for thePerception of Visual Motion is a unique reference for advancedstudents in electrical engineering, artificial intelligence,robotics and computational neuroscience. It will also be useful forresearchers, professionals, and electronics engineers working inthe field.

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  • Produktdetails
  • Verlag: John Wiley & Sons
  • Seitenzahl: 242
  • Erscheinungstermin: 30.03.2006
  • Englisch
  • ISBN-13: 9780470034880
  • Artikelnr.: 37290182
Autorenporträt
Alan A. Stocker is the author of Analog VLSI Circuits for the Perception of Visual Motion, published by Wiley.
Inhaltsangabe
Foreword. Preface. 1 Introduction. 1.1 Artificial Autonomous Systems. 1.2 Neural Computation and Analog Integrated Circuits. 2 Visual Motion Perception. 2.1 Image Brightness. 2.2 Correspondence Problem. 2.3 Optical Flow. 2.4 Matching Models. 2.4.1 Explicit matching. 2.4.2 Implicit matching. 2.5 FlowModels. 2.5.1 Global motion. 2.5.2 Local motion. 2.5.3 Perceptual bias. 2.6 Outline for a Visual Motion Perception System. 2.7 Review of aVLSI Implementations. 3 Optimization Networks. 3.1 AssociativeMemory and Optimization. 3.2 Constraint Satisfaction Problems. 3.3 Winner
takes
all Networks. 3.3.1 Network architecture. 3.3.2 Global convergence and gain. 3.4 Resistive Network. 4 Visual Motion Perception Networks. 4.1 Model for Optical Flow Estimation. 4.1.1 Well
posed optimization problem. 4.1.2 Mechanical equivalent. 4.1.3 Smoothness and sparse data. 4.1.4 Probabilistic formulation. 4.2 Network Architecture. 4.2.1 Non
stationary optimization. 4.2.2 Network conductances. 4.3 Simulation Results for Natural Image Sequences. 4.4 Passive Non
linear Network Conductances. 4.5 Extended Recurrent Network Architectures. 4.5.1 Motion segmentation. 4.5.2 Attention and motion selection. 4.6 Remarks. 5 Analog VLSI Implementation. 5.1 Implementation Substrate. 5.2 Phototransduction. 5.2.1 Logarithmic adaptive photoreceptor. 5.2.2 Robust brightness constancy constraint. 5.3 Extraction of the Spatio
temporal Brightness Gradients. 5.3.1 Temporal derivative circuits. 5.3.2 Spatial sampling. 5.4 Single Optical Flow Unit. 5.4.1 Wide
linear
range multiplier. 5.4.2 Effective bias conductance. 5.4.3 Implementation of the smoothness constraint. 5.5 Layout. 6 Smooth Optical Flow Chip. 6.1 Response Characteristics. 6.1.1 Speed tuning. 6.1.2 Contrast dependence. 6.1.3 Spatial frequency tuning. 6.1.4 Orientation tuning. 6.2 Intersection
of
constraints Solution. 6.3 Flow Field Estimation. 6.4 DeviceMismatch. 6.4.1 Gradient offsets. 6.4.2 Variations across the array. 6.5 Processing Speed. 6.6 Applications. 6.6.1 Sensor modules for robotic applications. 6.6.2 Human
machine interface. 7 Extended Network Implementations. 7.1 Motion Segmentation Chip. 7.1.1 Schematics of the motion segmentation pixel. 7.1.2 Experiments and results. 7.2 Motion Selection Chip. 7.2.1 Pixel schematics. 7.2.2 Non
linear diffusion length. 7.2.3 Experiments and results. 8 Comparison to Human Motion Vision. 8.1 Human vs. Chip Perception. 8.1.1 Contrast
dependent speed perception. 8.1.2 Bias on perceived direction of motion. 8.1.3 Perceptual dynamics. 8.2 Computational Architecture. 8.3 Remarks. Appendix. A Variational Calculus. B Simulation Methods. C Transistors and Basic Circuits. D Process Parameters and Chips Specifications. References. Index.
Rezensionen
"...provides unconventional and fresh perspectives on how to understand perception and build simple artificial perceptual systems using VLSI circuits." ( The Neurimorphic Engineer, March 2007